diff --git a/internal/ceres/cuda_buffer.h b/internal/ceres/cuda_buffer.h
index 8868e1a..64774fa 100644
--- a/internal/ceres/cuda_buffer.h
+++ b/internal/ceres/cuda_buffer.h
@@ -84,9 +84,10 @@
   // Perform an asynchronous copy from GPU memory using the stream provided.
   void CopyFromGpuAsync(const T* data, const size_t size, cudaStream_t stream) {
     Reserve(size);
-    CHECK_EQ(cudaMemcpyAsync(
-        data_, data, size * sizeof(T), cudaMemcpyDeviceToDevice, stream),
-            cudaSuccess);
+    CHECK_EQ(
+        cudaMemcpyAsync(
+            data_, data, size * sizeof(T), cudaMemcpyDeviceToDevice, stream),
+        cudaSuccess);
   }
 
   // Copy data from the GPU to CPU memory. This is necessarily synchronous since
diff --git a/internal/ceres/cuda_dense_cholesky_test.cc b/internal/ceres/cuda_dense_cholesky_test.cc
index b9acc99..c7b11ce 100644
--- a/internal/ceres/cuda_dense_cholesky_test.cc
+++ b/internal/ceres/cuda_dense_cholesky_test.cc
@@ -258,8 +258,6 @@
   EXPECT_NEAR(x(3), 1.0000, kEpsilon);
 }
 
-
-
 TEST(CUDADenseCholeskyMixedPrecision, Randomized1600x1600Tests) {
   const int kNumCols = 1600;
   using LhsType = Eigen::Matrix<double, Eigen::Dynamic, Eigen::Dynamic>;
diff --git a/internal/ceres/dense_cholesky.cc b/internal/ceres/dense_cholesky.cc
index e2c5036..16d3e1a 100644
--- a/internal/ceres/dense_cholesky.cc
+++ b/internal/ceres/dense_cholesky.cc
@@ -36,6 +36,7 @@
 #include <vector>
 
 #include "ceres/internal/config.h"
+#include "ceres/iterative_refiner.h"
 
 #ifndef CERES_NO_CUDA
 #include "ceres/ceres_cuda_kernels.h"
@@ -58,6 +59,18 @@
                         double* b,
                         const int* ldb,
                         int* info);
+
+extern "C" void spotrf_(
+    const char* uplo, const int* n, float* a, const int* lda, int* info);
+
+extern "C" void spotrs_(const char* uplo,
+                        const int* n,
+                        const int* nrhs,
+                        const float* a,
+                        const int* lda,
+                        float* b,
+                        const int* ldb,
+                        int* info);
 #endif
 
 namespace ceres::internal {
@@ -69,17 +82,23 @@
   std::unique_ptr<DenseCholesky> dense_cholesky;
 
   switch (options.dense_linear_algebra_library_type) {
-    case EIGEN: {
+    case EIGEN:
       // Eigen mixed precision solver not yet implemented.
-      if (options.use_mixed_precision_solves) return nullptr;
-      dense_cholesky = std::make_unique<EigenDenseCholesky>();
-    } break;
+      if (options.use_mixed_precision_solves) {
+        dense_cholesky = std::make_unique<FloatEigenDenseCholesky>();
+      } else {
+        dense_cholesky = std::make_unique<EigenDenseCholesky>();
+      }
+      break;
 
     case LAPACK:
 #ifndef CERES_NO_LAPACK
       // LAPACK mixed precision solver not yet implemented.
-      if (options.use_mixed_precision_solves) return nullptr;
-      dense_cholesky = std::make_unique<LAPACKDenseCholesky>();
+      if (options.use_mixed_precision_solves) {
+        dense_cholesky = std::make_unique<FloatLAPACKDenseCholesky>();
+      } else {
+        dense_cholesky = std::make_unique<LAPACKDenseCholesky>();
+      }
       break;
 #else
       LOG(FATAL) << "Ceres was compiled without support for LAPACK.";
@@ -102,6 +121,14 @@
                  << DenseLinearAlgebraLibraryTypeToString(
                         options.dense_linear_algebra_library_type);
   }
+
+  if (options.max_num_refinement_iterations > 0) {
+    auto refiner = std::make_unique<DenseIterativeRefiner>(
+        options.max_num_refinement_iterations);
+    dense_cholesky = std::make_unique<RefinedDenseCholesky>(
+        std::move(dense_cholesky), std::move(refiner));
+  }
+
   return dense_cholesky;
 }
 
@@ -146,6 +173,34 @@
   return LinearSolverTerminationType::SUCCESS;
 }
 
+LinearSolverTerminationType FloatEigenDenseCholesky::Factorize(
+    int num_cols, double* lhs, std::string* message) {
+  // TODO(sameeragarwal): Check if this causes a double allocation.
+  lhs_ = Eigen::Map<Eigen::MatrixXd>(lhs, num_cols, num_cols).cast<float>();
+  llt_ = std::make_unique<LLTType>(lhs_);
+  if (llt_->info() != Eigen::Success) {
+    *message = "Eigen failure. Unable to perform dense Cholesky factorization.";
+    return LinearSolverTerminationType::FAILURE;
+  }
+
+  *message = "Success.";
+  return LinearSolverTerminationType::SUCCESS;
+}
+
+LinearSolverTerminationType FloatEigenDenseCholesky::Solve(
+    const double* rhs, double* solution, std::string* message) {
+  if (llt_->info() != Eigen::Success) {
+    *message = "Eigen failure. Unable to perform dense Cholesky factorization.";
+    return LinearSolverTerminationType::FAILURE;
+  }
+
+  rhs_ = ConstVectorRef(rhs, llt_->cols()).cast<float>();
+  solution_ = llt_->solve(rhs_);
+  VectorRef(solution, llt_->cols()) = solution_.cast<double>();
+  *message = "Success.";
+  return LinearSolverTerminationType::SUCCESS;
+}
+
 #ifndef CERES_NO_LAPACK
 LinearSolverTerminationType LAPACKDenseCholesky::Factorize(
     int num_cols, double* lhs, std::string* message) {
@@ -182,7 +237,7 @@
   const int nrhs = 1;
   int info = 0;
 
-  std::copy_n(rhs, num_cols_, solution);
+  VectorRef(solution, num_cols_) = ConstVectorRef(rhs, num_cols_);
   dpotrs_(
       &uplo, &num_cols_, &nrhs, lhs_, &num_cols_, solution, &num_cols_, &info);
 
@@ -200,8 +255,95 @@
   return termination_type_;
 }
 
+LinearSolverTerminationType FloatLAPACKDenseCholesky::Factorize(
+    int num_cols, double* lhs, std::string* message) {
+  num_cols_ = num_cols;
+  lhs_ = Eigen::Map<Eigen::MatrixXd>(lhs, num_cols, num_cols).cast<float>();
+
+  const char uplo = 'L';
+  int info = 0;
+  spotrf_(&uplo, &num_cols_, lhs_.data(), &num_cols_, &info);
+
+  if (info < 0) {
+    termination_type_ = LinearSolverTerminationType::FATAL_ERROR;
+    LOG(FATAL) << "Congratulations, you found a bug in Ceres. "
+               << "Please report it. "
+               << "LAPACK::spotrf fatal error. "
+               << "Argument: " << -info << " is invalid.";
+  } else if (info > 0) {
+    termination_type_ = LinearSolverTerminationType::FAILURE;
+    *message = StringPrintf(
+        "LAPACK::spotrf numerical failure. "
+        "The leading minor of order %d is not positive definite.",
+        info);
+  } else {
+    termination_type_ = LinearSolverTerminationType::SUCCESS;
+    *message = "Success.";
+  }
+  return termination_type_;
+}
+
+LinearSolverTerminationType FloatLAPACKDenseCholesky::Solve(
+    const double* rhs, double* solution, std::string* message) {
+  const char uplo = 'L';
+  const int nrhs = 1;
+  int info = 0;
+  rhs_and_solution_ = ConstVectorRef(rhs, num_cols_).cast<float>();
+  spotrs_(&uplo,
+          &num_cols_,
+          &nrhs,
+          lhs_.data(),
+          &num_cols_,
+          rhs_and_solution_.data(),
+          &num_cols_,
+          &info);
+
+  if (info < 0) {
+    termination_type_ = LinearSolverTerminationType::FATAL_ERROR;
+    LOG(FATAL) << "Congratulations, you found a bug in Ceres. "
+               << "Please report it. "
+               << "LAPACK::dpotrs fatal error. "
+               << "Argument: " << -info << " is invalid.";
+  }
+
+  *message = "Success";
+  termination_type_ = LinearSolverTerminationType::SUCCESS;
+  VectorRef(solution, num_cols_) =
+      rhs_and_solution_.head(num_cols_).cast<double>();
+  return termination_type_;
+}
+
 #endif  // CERES_NO_LAPACK
 
+RefinedDenseCholesky::RefinedDenseCholesky(
+    std::unique_ptr<DenseCholesky> dense_cholesky,
+    std::unique_ptr<DenseIterativeRefiner> iterative_refiner)
+    : dense_cholesky_(std::move(dense_cholesky)),
+      iterative_refiner_(std::move(iterative_refiner)) {}
+
+RefinedDenseCholesky::~RefinedDenseCholesky() = default;
+
+LinearSolverTerminationType RefinedDenseCholesky::Factorize(
+    const int num_cols, double* lhs, std::string* message) {
+  lhs_ = lhs;
+  num_cols_ = num_cols;
+  return dense_cholesky_->Factorize(num_cols, lhs, message);
+}
+
+LinearSolverTerminationType RefinedDenseCholesky::Solve(const double* rhs,
+                                                        double* solution,
+                                                        std::string* message) {
+  CHECK(lhs_ != nullptr);
+  auto termination_type = dense_cholesky_->Solve(rhs, solution, message);
+  if (termination_type != LinearSolverTerminationType::SUCCESS) {
+    return termination_type;
+  }
+
+  iterative_refiner_->Refine(
+      num_cols_, lhs_, rhs, dense_cholesky_.get(), solution);
+  return LinearSolverTerminationType::SUCCESS;
+}
+
 #ifndef CERES_NO_CUDA
 
 bool CUDADenseCholesky::Init(ContextImpl* context, std::string* message) {
@@ -323,15 +465,14 @@
   if (cuda_dense_cholesky->Init(options.context, &cuda_error)) {
     return cuda_dense_cholesky;
   }
-  // Initialization failed, destroy the object (done automatically) and return a
-  // nullptr.
+  // Initialization failed, destroy the object (done automatically) and return
+  // a nullptr.
   LOG(ERROR) << "CUDADenseCholesky::Init failed: " << cuda_error;
   return nullptr;
 }
 
 std::unique_ptr<CUDADenseCholeskyMixedPrecision>
-    CUDADenseCholeskyMixedPrecision::Create(
-    const LinearSolver::Options& options) {
+CUDADenseCholeskyMixedPrecision::Create(const LinearSolver::Options& options) {
   if (options.dense_linear_algebra_library_type != CUDA ||
       !options.use_mixed_precision_solves) {
     // The user called the wrong factory method.
@@ -347,8 +488,8 @@
   return nullptr;
 }
 
-bool CUDADenseCholeskyMixedPrecision::Init(
-    const LinearSolver::Options& options, std::string* message) {
+bool CUDADenseCholeskyMixedPrecision::Init(const LinearSolver::Options& options,
+                                           std::string* message) {
   if (!options.context->InitCUDA(message)) {
     return false;
   }
@@ -452,9 +593,7 @@
 }
 
 LinearSolverTerminationType CUDADenseCholeskyMixedPrecision::Factorize(
-    int num_cols,
-    double* lhs,
-    std::string* message) {
+    int num_cols, double* lhs, std::string* message) {
   num_cols_ = num_cols;
 
   // Copy fp64 version of lhs to GPU.
@@ -472,9 +611,7 @@
 }
 
 LinearSolverTerminationType CUDADenseCholeskyMixedPrecision::Solve(
-    const double* rhs,
-    double* solution,
-    std::string* message) {
+    const double* rhs, double* solution, std::string* message) {
   // If factorization failed, return failure.
   if (factorize_result_ != LinearSolverTerminationType::SUCCESS) {
     *message = "Factorize did not complete successfully previously.";
@@ -505,8 +642,7 @@
       return result;
     }
     // [fp64] x += c.
-    CudaDsxpy(
-        x_fp64_.data(), correction_fp32_.data(), num_cols_, stream_);
+    CudaDsxpy(x_fp64_.data(), correction_fp32_.data(), num_cols_, stream_);
     if (i < max_num_refinement_iterations_) {
       // [fp64] residual = rhs - lhs * x
       // This is done in two steps:
diff --git a/internal/ceres/dense_cholesky.h b/internal/ceres/dense_cholesky.h
index 0593875..1c561c1 100644
--- a/internal/ceres/dense_cholesky.h
+++ b/internal/ceres/dense_cholesky.h
@@ -114,6 +114,23 @@
   std::unique_ptr<LLTType> llt_;
 };
 
+class CERES_NO_EXPORT FloatEigenDenseCholesky final : public DenseCholesky {
+ public:
+  LinearSolverTerminationType Factorize(int num_cols,
+                                        double* lhs,
+                                        std::string* message) override;
+  LinearSolverTerminationType Solve(const double* rhs,
+                                    double* solution,
+                                    std::string* message) override;
+
+ private:
+  Eigen::MatrixXf lhs_;
+  Eigen::VectorXf rhs_;
+  Eigen::VectorXf solution_;
+  using LLTType = Eigen::LLT<Eigen::MatrixXf, Eigen::Lower>;
+  std::unique_ptr<LLTType> llt_;
+};
+
 #ifndef CERES_NO_LAPACK
 class CERES_NO_EXPORT LAPACKDenseCholesky final : public DenseCholesky {
  public:
@@ -130,8 +147,50 @@
   LinearSolverTerminationType termination_type_ =
       LinearSolverTerminationType::FATAL_ERROR;
 };
+
+class CERES_NO_EXPORT FloatLAPACKDenseCholesky final : public DenseCholesky {
+ public:
+  LinearSolverTerminationType Factorize(int num_cols,
+                                        double* lhs,
+                                        std::string* message) override;
+  LinearSolverTerminationType Solve(const double* rhs,
+                                    double* solution,
+                                    std::string* message) override;
+
+ private:
+  Eigen::MatrixXf lhs_;
+  Eigen::VectorXf rhs_and_solution_;
+  int num_cols_ = -1;
+  LinearSolverTerminationType termination_type_ =
+      LinearSolverTerminationType::FATAL_ERROR;
+};
 #endif  // CERES_NO_LAPACK
 
+class DenseIterativeRefiner;
+
+// Computes an initial solution using the given instance of
+// DenseCholesky, and then refines it using the DenseIterativeRefiner.
+class CERES_NO_EXPORT RefinedDenseCholesky final : public DenseCholesky {
+ public:
+  RefinedDenseCholesky(
+      std::unique_ptr<DenseCholesky> dense_cholesky,
+      std::unique_ptr<DenseIterativeRefiner> iterative_refiner);
+  ~RefinedDenseCholesky() override;
+
+  LinearSolverTerminationType Factorize(int num_cols,
+                                        double* lhs,
+                                        std::string* message) override;
+  LinearSolverTerminationType Solve(const double* rhs,
+                                    double* solution,
+                                    std::string* message) override;
+
+ private:
+  std::unique_ptr<DenseCholesky> dense_cholesky_;
+  std::unique_ptr<DenseIterativeRefiner> iterative_refiner_;
+  double* lhs_ = nullptr;
+  int num_cols_;
+};
+
 #ifndef CERES_NO_CUDA
 // CUDA implementation of DenseCholesky using the cuSolverDN library using the
 // 32-bit legacy interface for maximum compatibility.
@@ -190,13 +249,13 @@
 //    symmetric positive definite.
 // 2. During the solution update, the up-cast and accumulation is performed in
 //    one step with a custom kernel.
-class CERES_NO_EXPORT CUDADenseCholeskyMixedPrecision final :
-    public DenseCholesky {
+class CERES_NO_EXPORT CUDADenseCholeskyMixedPrecision final
+    : public DenseCholesky {
  public:
   static std::unique_ptr<CUDADenseCholeskyMixedPrecision> Create(
       const LinearSolver::Options& options);
-  CUDADenseCholeskyMixedPrecision(
-      const CUDADenseCholeskyMixedPrecision&) = delete;
+  CUDADenseCholeskyMixedPrecision(const CUDADenseCholeskyMixedPrecision&) =
+      delete;
   CUDADenseCholeskyMixedPrecision& operator=(
       const CUDADenseCholeskyMixedPrecision&) = delete;
   LinearSolverTerminationType Factorize(int num_cols,
diff --git a/internal/ceres/dense_cholesky_test.cc b/internal/ceres/dense_cholesky_test.cc
index 5f96939..f8e6567 100644
--- a/internal/ceres/dense_cholesky_test.cc
+++ b/internal/ceres/dense_cholesky_test.cc
@@ -39,6 +39,7 @@
 #include "Eigen/Dense"
 #include "ceres/internal/config.h"
 #include "ceres/internal/eigen.h"
+#include "ceres/iterative_refiner.h"
 #include "ceres/linear_solver.h"
 #include "glog/logging.h"
 #include "gmock/gmock.h"
@@ -80,8 +81,7 @@
   if (options.use_mixed_precision_solves) {
     options.max_num_refinement_iterations = kNumRefinementSteps;
   }
-  std::unique_ptr<DenseCholesky> dense_cholesky =
-      DenseCholesky::Create(options);
+  auto dense_cholesky = DenseCholesky::Create(options);
 
   const int kNumTrials = 10;
   const int kMinNumCols = 1;
@@ -111,13 +111,15 @@
 INSTANTIATE_TEST_SUITE_P(EigenCholesky,
                          DenseCholeskyTest,
                          ::testing::Combine(::testing::Values(EIGEN),
-                                            ::testing::Values(kFullPrecision)),
+                                            ::testing::Values(kMixedPrecision,
+                                                              kFullPrecision)),
                          ParamInfoToString);
 #ifndef CERES_NO_LAPACK
 INSTANTIATE_TEST_SUITE_P(LapackCholesky,
                          DenseCholeskyTest,
                          ::testing::Combine(::testing::Values(LAPACK),
-                                            ::testing::Values(kFullPrecision)),
+                                            ::testing::Values(kMixedPrecision,
+                                                              kFullPrecision)),
                          ParamInfoToString);
 #endif
 #ifndef CERES_NO_CUDA
@@ -129,44 +131,86 @@
                          ParamInfoToString);
 #endif
 
-#ifndef CERES_NO_CUDA
-TEST(DenseCholesky, ValidMixedPrecisionOptions) {
-  // Dense Cholesky with CUDA: okay, supported.
-  ContextImpl context;
-  LinearSolver::Options options;
-  options.dense_linear_algebra_library_type = CUDA;
-  options.use_mixed_precision_solves = true;
-  options.context = &context;
-  std::unique_ptr<DenseCholesky> dense_cholesky =
-      DenseCholesky::Create(options);
-  EXPECT_NE(dense_cholesky, nullptr);
-}
-#endif
+class MockDenseCholesky : public DenseCholesky {
+ public:
+  MOCK_METHOD3(Factorize,
+               LinearSolverTerminationType(int num_cols,
+                                           double* lhs,
+                                           std::string* message));
+  MOCK_METHOD3(Solve,
+               LinearSolverTerminationType(const double* rhs,
+                                           double* solution,
+                                           std::string* message));
+};
 
-TEST(DenseCholesky, InvalidMixedPrecisionOptionsEigen) {
-  // Dense Cholesky with Eigen: not supported
-  ContextImpl context;
-  LinearSolver::Options options;
-  options.dense_linear_algebra_library_type = EIGEN;
-  options.use_mixed_precision_solves = true;
-  options.context = &context;
-  std::unique_ptr<DenseCholesky> dense_cholesky =
-      DenseCholesky::Create(options);
-  EXPECT_EQ(dense_cholesky, nullptr);
-}
+class MockDenseIterativeRefiner : public DenseIterativeRefiner {
+ public:
+  MockDenseIterativeRefiner() : DenseIterativeRefiner(1) {}
+  MOCK_METHOD5(Refine,
+               void(int num_cols,
+                    const double* lhs,
+                    const double* rhs,
+                    DenseCholesky* dense_cholesky,
+                    double* solution));
+};
 
-#ifndef CERES_NO_LAPACK
-TEST(DenseCholesky, InvalidMixedPrecisionOptionsLAPACK) {
-  // Dense Cholesky with Lapack: not supported
-  ContextImpl context;
-  LinearSolver::Options options;
-  options.dense_linear_algebra_library_type = LAPACK;
-  options.use_mixed_precision_solves = true;
-  options.context = &context;
-  std::unique_ptr<DenseCholesky> dense_cholesky =
-      DenseCholesky::Create(options);
-  EXPECT_EQ(dense_cholesky, nullptr);
-}
-#endif
+using testing::_;
+using testing::Return;
+
+TEST(RefinedDenseCholesky, Factorize) {
+  auto dense_cholesky = std::make_unique<MockDenseCholesky>();
+  auto iterative_refiner = std::make_unique<MockDenseIterativeRefiner>();
+  EXPECT_CALL(*dense_cholesky, Factorize(_, _, _))
+      .Times(1)
+      .WillRepeatedly(Return(LinearSolverTerminationType::SUCCESS));
+  EXPECT_CALL(*iterative_refiner, Refine(_, _, _, _, _)).Times(0);
+  RefinedDenseCholesky refined_dense_cholesky(std::move(dense_cholesky),
+                                              std::move(iterative_refiner));
+  double lhs;
+  std::string message;
+  EXPECT_EQ(refined_dense_cholesky.Factorize(1, &lhs, &message),
+            LinearSolverTerminationType::SUCCESS);
+};
+
+TEST(RefinedDenseCholesky, FactorAndSolveWithUnsuccessfulFactorization) {
+  auto dense_cholesky = std::make_unique<MockDenseCholesky>();
+  auto iterative_refiner = std::make_unique<MockDenseIterativeRefiner>();
+  EXPECT_CALL(*dense_cholesky, Factorize(_, _, _))
+      .Times(1)
+      .WillRepeatedly(Return(LinearSolverTerminationType::FAILURE));
+  EXPECT_CALL(*dense_cholesky, Solve(_, _, _)).Times(0);
+  EXPECT_CALL(*iterative_refiner, Refine(_, _, _, _, _)).Times(0);
+  RefinedDenseCholesky refined_dense_cholesky(std::move(dense_cholesky),
+                                              std::move(iterative_refiner));
+  double lhs;
+  std::string message;
+  double rhs;
+  double solution;
+  EXPECT_EQ(
+      refined_dense_cholesky.FactorAndSolve(1, &lhs, &rhs, &solution, &message),
+      LinearSolverTerminationType::FAILURE);
+};
+
+TEST(RefinedDenseCholesky, FactorAndSolveWithSuccess) {
+  auto dense_cholesky = std::make_unique<MockDenseCholesky>();
+  auto iterative_refiner = std::make_unique<MockDenseIterativeRefiner>();
+  EXPECT_CALL(*dense_cholesky, Factorize(_, _, _))
+      .Times(1)
+      .WillRepeatedly(Return(LinearSolverTerminationType::SUCCESS));
+  EXPECT_CALL(*dense_cholesky, Solve(_, _, _))
+      .Times(1)
+      .WillRepeatedly(Return(LinearSolverTerminationType::SUCCESS));
+  EXPECT_CALL(*iterative_refiner, Refine(_, _, _, _, _)).Times(1);
+
+  RefinedDenseCholesky refined_dense_cholesky(std::move(dense_cholesky),
+                                              std::move(iterative_refiner));
+  double lhs;
+  std::string message;
+  double rhs;
+  double solution;
+  EXPECT_EQ(
+      refined_dense_cholesky.FactorAndSolve(1, &lhs, &rhs, &solution, &message),
+      LinearSolverTerminationType::SUCCESS);
+};
 
 }  // namespace ceres::internal
diff --git a/internal/ceres/eigensparse.cc b/internal/ceres/eigensparse.cc
index a2407c0..ce01658 100644
--- a/internal/ceres/eigensparse.cc
+++ b/internal/ceres/eigensparse.cc
@@ -195,7 +195,7 @@
 #else
     LOG(FATAL)
         << "Congratulations you have found a bug in Ceres Solver. Please "
-        "report it to the Ceres Solver developers.";
+           "report it to the Ceres Solver developers.";
     return nullptr;
 #endif  // CERES_NO_EIGEN_METIS
   }
diff --git a/internal/ceres/iterative_refiner.cc b/internal/ceres/iterative_refiner.cc
index b9fa88a..90ff511 100644
--- a/internal/ceres/iterative_refiner.cc
+++ b/internal/ceres/iterative_refiner.cc
@@ -33,39 +33,67 @@
 #include <string>
 
 #include "Eigen/Core"
+#include "ceres/dense_cholesky.h"
 #include "ceres/sparse_cholesky.h"
 #include "ceres/sparse_matrix.h"
 
 namespace ceres::internal {
 
-IterativeRefiner::IterativeRefiner(const int max_num_iterations)
+SparseIterativeRefiner::SparseIterativeRefiner(const int max_num_iterations)
     : max_num_iterations_(max_num_iterations) {}
 
-IterativeRefiner::~IterativeRefiner() = default;
+SparseIterativeRefiner::~SparseIterativeRefiner() = default;
 
-void IterativeRefiner::Allocate(int num_cols) {
+void SparseIterativeRefiner::Allocate(int num_cols) {
   residual_.resize(num_cols);
   correction_.resize(num_cols);
   lhs_x_solution_.resize(num_cols);
 }
 
-void IterativeRefiner::Refine(const SparseMatrix& lhs,
-                              const double* rhs_ptr,
-                              SparseCholesky* sparse_cholesky,
-                              double* solution_ptr) {
+void SparseIterativeRefiner::Refine(const SparseMatrix& lhs,
+                                    const double* rhs_ptr,
+                                    SparseCholesky* cholesky,
+                                    double* solution_ptr) {
   const int num_cols = lhs.num_cols();
   Allocate(num_cols);
   ConstVectorRef rhs(rhs_ptr, num_cols);
   VectorRef solution(solution_ptr, num_cols);
+  std::string ignored_message;
   for (int i = 0; i < max_num_iterations_; ++i) {
     // residual = rhs - lhs * solution
     lhs_x_solution_.setZero();
     lhs.RightMultiply(solution_ptr, lhs_x_solution_.data());
     residual_ = rhs - lhs_x_solution_;
     // solution += lhs^-1 residual
-    std::string ignored_message;
-    sparse_cholesky->Solve(
-        residual_.data(), correction_.data(), &ignored_message);
+    cholesky->Solve(residual_.data(), correction_.data(), &ignored_message);
+    solution += correction_;
+  }
+};
+
+DenseIterativeRefiner::DenseIterativeRefiner(const int max_num_iterations)
+    : max_num_iterations_(max_num_iterations) {}
+
+DenseIterativeRefiner::~DenseIterativeRefiner() = default;
+
+void DenseIterativeRefiner::Allocate(int num_cols) {
+  residual_.resize(num_cols);
+  correction_.resize(num_cols);
+}
+
+void DenseIterativeRefiner::Refine(const int num_cols,
+                                   const double* lhs_ptr,
+                                   const double* rhs_ptr,
+                                   DenseCholesky* cholesky,
+                                   double* solution_ptr) {
+  Allocate(num_cols);
+  ConstMatrixRef lhs(lhs_ptr, num_cols, num_cols);
+  ConstVectorRef rhs(rhs_ptr, num_cols);
+  VectorRef solution(solution_ptr, num_cols);
+  std::string ignored_message;
+  for (int i = 0; i < max_num_iterations_; ++i) {
+    residual_ = rhs - lhs * solution;
+    // solution += lhs^-1 residual
+    cholesky->Solve(residual_.data(), correction_.data(), &ignored_message);
     solution += correction_;
   }
 };
diff --git a/internal/ceres/iterative_refiner.h b/internal/ceres/iterative_refiner.h
index d500212..8333124 100644
--- a/internal/ceres/iterative_refiner.h
+++ b/internal/ceres/iterative_refiner.h
@@ -41,6 +41,7 @@
 
 namespace ceres::internal {
 
+class DenseCholesky;
 class SparseCholesky;
 class SparseMatrix;
 
@@ -57,20 +58,20 @@
 // Definite linear systems.
 //
 // The above iterative loop is run until max_num_iterations is reached.
-class CERES_NO_EXPORT IterativeRefiner {
+class CERES_NO_EXPORT SparseIterativeRefiner {
  public:
   // max_num_iterations is the number of refinement iterations to
   // perform.
-  explicit IterativeRefiner(int max_num_iterations);
+  explicit SparseIterativeRefiner(int max_num_iterations);
 
   // Needed for mocking.
-  virtual ~IterativeRefiner();
+  virtual ~SparseIterativeRefiner();
 
   // Given an initial estimate of the solution of lhs * x = rhs, use
   // max_num_iterations rounds of iterative refinement to improve it.
   //
-  // sparse_cholesky is assumed to contain an already computed
-  // factorization (or approximation thereof) of lhs.
+  // cholesky is assumed to contain an already computed factorization (or
+  // an approximation thereof) of lhs.
   //
   // solution is expected to contain a approximation to the solution
   // to lhs * x = rhs. It can be zero.
@@ -78,7 +79,7 @@
   // This method is virtual to facilitate mocking.
   virtual void Refine(const SparseMatrix& lhs,
                       const double* rhs,
-                      SparseCholesky* sparse_cholesky,
+                      SparseCholesky* cholesky,
                       double* solution);
 
  private:
@@ -90,6 +91,39 @@
   Vector lhs_x_solution_;
 };
 
+class CERES_NO_EXPORT DenseIterativeRefiner {
+ public:
+  // max_num_iterations is the number of refinement iterations to
+  // perform.
+  explicit DenseIterativeRefiner(int max_num_iterations);
+
+  // Needed for mocking.
+  virtual ~DenseIterativeRefiner();
+
+  // Given an initial estimate of the solution of lhs * x = rhs, use
+  // max_num_iterations rounds of iterative refinement to improve it.
+  //
+  // cholesky is assumed to contain an already computed factorization (or
+  // an approximation thereof) of lhs.
+  //
+  // solution is expected to contain a approximation to the solution
+  // to lhs * x = rhs. It can be zero.
+  //
+  // This method is virtual to facilitate mocking.
+  virtual void Refine(int num_cols,
+                      const double* lhs,
+                      const double* rhs,
+                      DenseCholesky* cholesky,
+                      double* solution);
+
+ private:
+  void Allocate(int num_cols);
+
+  int max_num_iterations_;
+  Vector residual_;
+  Vector correction_;
+};
+
 }  // namespace ceres::internal
 
 #endif  // CERES_INTERNAL_ITERATIVE_REFINER_H_
diff --git a/internal/ceres/iterative_refiner_test.cc b/internal/ceres/iterative_refiner_test.cc
index 5718f14..0b09247 100644
--- a/internal/ceres/iterative_refiner_test.cc
+++ b/internal/ceres/iterative_refiner_test.cc
@@ -33,6 +33,7 @@
 #include <utility>
 
 #include "Eigen/Dense"
+#include "ceres/dense_cholesky.h"
 #include "ceres/internal/eigen.h"
 #include "ceres/sparse_cholesky.h"
 #include "ceres/sparse_matrix.h"
@@ -97,7 +98,9 @@
     const int num_cols = lhs_.cols();
     VectorRef solution(solution_ptr, num_cols);
     ConstVectorRef rhs(rhs_ptr, num_cols);
-    solution = lhs_.llt().solve(rhs.cast<Scalar>()).template cast<double>();
+    auto llt = lhs_.llt();
+    CHECK_NE(llt.info(), Eigen::Success);
+    solution = llt.solve(rhs.cast<Scalar>()).template cast<double>();
     return LinearSolverTerminationType::SUCCESS;
   }
 
@@ -113,10 +116,37 @@
   Eigen::Matrix<Scalar, Eigen::Dynamic, Eigen::Dynamic> lhs_;
 };
 
+// A fake DenseCholesky which uses Eigen's Cholesky factorization to
+// do the real work. The template parameter allows us to work in
+// doubles or floats, even though the source matrix is double.
+template <typename Scalar>
+class FakeDenseCholesky : public DenseCholesky {
+ public:
+  explicit FakeDenseCholesky(const Matrix& lhs) { lhs_ = lhs.cast<Scalar>(); }
+
+  LinearSolverTerminationType Solve(const double* rhs_ptr,
+                                    double* solution_ptr,
+                                    std::string* message) final {
+    const int num_cols = lhs_.cols();
+    VectorRef solution(solution_ptr, num_cols);
+    ConstVectorRef rhs(rhs_ptr, num_cols);
+    solution = lhs_.llt().solve(rhs.cast<Scalar>()).template cast<double>();
+    return LinearSolverTerminationType::SUCCESS;
+  }
+
+  LinearSolverTerminationType Factorize(int num_cols,
+                                        double* lhs,
+                                        std::string* message) final
+      DO_NOT_CALL_WITH_RETURN(LinearSolverTerminationType::FAILURE);
+
+ private:
+  Eigen::Matrix<Scalar, Eigen::Dynamic, Eigen::Dynamic> lhs_;
+};
+
 #undef DO_NOT_CALL
 #undef DO_NOT_CALL_WITH_RETURN
 
-class IterativeRefinerTest : public ::testing::Test {
+class SparseIterativeRefinerTest : public ::testing::Test {
  public:
   void SetUp() override {
     num_cols_ = 5;
@@ -136,10 +166,11 @@
   Vector rhs_, solution_;
 };
 
-TEST_F(IterativeRefinerTest, RandomSolutionWithExactFactorizationConverges) {
+TEST_F(SparseIterativeRefinerTest,
+       RandomSolutionWithExactFactorizationConverges) {
   FakeSparseMatrix lhs(lhs_);
   FakeSparseCholesky<double> sparse_cholesky(lhs_);
-  IterativeRefiner refiner(max_num_iterations_);
+  SparseIterativeRefiner refiner(max_num_iterations_);
   Vector refined_solution(num_cols_);
   refined_solution.setRandom();
   refiner.Refine(lhs, rhs_.data(), &sparse_cholesky, refined_solution.data());
@@ -148,13 +179,13 @@
               std::numeric_limits<double>::epsilon() * 10);
 }
 
-TEST_F(IterativeRefinerTest,
+TEST_F(SparseIterativeRefinerTest,
        RandomSolutionWithApproximationFactorizationConverges) {
   FakeSparseMatrix lhs(lhs_);
   // Use a single precision Cholesky factorization of the double
   // precision matrix. This will give us an approximate factorization.
   FakeSparseCholesky<float> sparse_cholesky(lhs_);
-  IterativeRefiner refiner(max_num_iterations_);
+  SparseIterativeRefiner refiner(max_num_iterations_);
   Vector refined_solution(num_cols_);
   refined_solution.setRandom();
   refiner.Refine(lhs, rhs_.data(), &sparse_cholesky, refined_solution.data());
@@ -163,4 +194,60 @@
               std::numeric_limits<double>::epsilon() * 10);
 }
 
+class DenseIterativeRefinerTest : public ::testing::Test {
+ public:
+  void SetUp() override {
+    num_cols_ = 5;
+    max_num_iterations_ = 30;
+    Matrix m(num_cols_, num_cols_);
+    m.setRandom();
+    lhs_ = m * m.transpose();
+    solution_.resize(num_cols_);
+    solution_.setRandom();
+    rhs_ = lhs_ * solution_;
+  };
+
+ protected:
+  int num_cols_;
+  int max_num_iterations_;
+  Matrix lhs_;
+  Vector rhs_, solution_;
+};
+
+TEST_F(DenseIterativeRefinerTest,
+       RandomSolutionWithExactFactorizationConverges) {
+  Matrix lhs = lhs_;
+  FakeDenseCholesky<double> dense_cholesky(lhs);
+  DenseIterativeRefiner refiner(max_num_iterations_);
+  Vector refined_solution(num_cols_);
+  refined_solution.setRandom();
+  refiner.Refine(lhs.cols(),
+                 lhs.data(),
+                 rhs_.data(),
+                 &dense_cholesky,
+                 refined_solution.data());
+  EXPECT_NEAR((lhs_ * refined_solution - rhs_).norm(),
+              0.0,
+              std::numeric_limits<double>::epsilon() * 10);
+}
+
+TEST_F(DenseIterativeRefinerTest,
+       RandomSolutionWithApproximationFactorizationConverges) {
+  Matrix lhs = lhs_;
+  // Use a single precision Cholesky factorization of the double
+  // precision matrix. This will give us an approximate factorization.
+  FakeDenseCholesky<float> dense_cholesky(lhs_);
+  DenseIterativeRefiner refiner(max_num_iterations_);
+  Vector refined_solution(num_cols_);
+  refined_solution.setRandom();
+  refiner.Refine(lhs.cols(),
+                 lhs.data(),
+                 rhs_.data(),
+                 &dense_cholesky,
+                 refined_solution.data());
+  EXPECT_NEAR((lhs_ * refined_solution - rhs_).norm(),
+              0.0,
+              std::numeric_limits<double>::epsilon() * 10);
+}
+
 }  // namespace ceres::internal
diff --git a/internal/ceres/solver.cc b/internal/ceres/solver.cc
index 93551f7..966a5dd 100644
--- a/internal/ceres/solver.cc
+++ b/internal/ceres/solver.cc
@@ -120,44 +120,36 @@
 
 bool MixedPrecisionOptionIsValid(const Solver::Options& options,
                                  string* error) {
-  if (options.use_mixed_precision_solves) {
-    if ((options.linear_solver_type == DENSE_NORMAL_CHOLESKY ||
-        options.linear_solver_type == DENSE_SCHUR) &&
-        options.dense_linear_algebra_library_type == CUDA) {
-      // Mixed precision with CUDA and dense Cholesky variant: okay.
-      return true;
-    }
-    if ((options.linear_solver_type == SPARSE_NORMAL_CHOLESKY ||
-        options.linear_solver_type == SPARSE_SCHUR) &&
-        (options.sparse_linear_algebra_library_type == EIGEN_SPARSE ||
-        options.sparse_linear_algebra_library_type == ACCELERATE_SPARSE)) {
+  if (!options.use_mixed_precision_solves) {
+    return true;
+  }
+
+  // All dense linear algebra backends support mixed precision solves now with
+  // Cholesky factorization.
+  if ((options.linear_solver_type == DENSE_NORMAL_CHOLESKY ||
+       options.linear_solver_type == DENSE_SCHUR)) {
+    return true;
+  }
+
+  if ((options.linear_solver_type == SPARSE_NORMAL_CHOLESKY ||
+       options.linear_solver_type == SPARSE_SCHUR)) {
+    if (options.sparse_linear_algebra_library_type == EIGEN_SPARSE ||
+        options.sparse_linear_algebra_library_type == ACCELERATE_SPARSE) {
       // Mixed precision with any Eigen or Accelerate Cholesky variant: okay.
       return true;
     }
-    // No other mixed precision variants are supported.
-    if (options.linear_solver_type == DENSE_NORMAL_CHOLESKY ||
-        options.linear_solver_type == DENSE_SCHUR) {
+    if (options.sparse_linear_algebra_library_type == SUITE_SPARSE) {
       *error = StringPrintf(
-          "use_mixed_precision_solves with %s is only supported with "
-          "CUDA as the dense_linear_algebra_library_type.",
-          LinearSolverTypeToString(options.linear_solver_type));
-      return false;
-    }
-    if ((options.linear_solver_type == SPARSE_NORMAL_CHOLESKY ||
-        options.linear_solver_type == SPARSE_SCHUR) &&
-        options.sparse_linear_algebra_library_type == SUITE_SPARSE) {
-      *error =  StringPrintf(
           "use_mixed_precision_solves with %s is not supported with "
           "SUITE_SPARSE as the sparse_linear_algebra_library_type.",
           LinearSolverTypeToString(options.linear_solver_type));
       return false;
     }
-    *error = StringPrintf(
-          "use_mixed_precision_solves with %s is not supported.",
-          LinearSolverTypeToString(options.linear_solver_type));
-    return false;
   }
-  return true;
+
+  *error = StringPrintf("use_mixed_precision_solves with %s is not supported.",
+                        LinearSolverTypeToString(options.linear_solver_type));
+  return false;
 }
 
 bool TrustRegionOptionsAreValid(const Solver::Options& options, string* error) {
@@ -300,7 +292,8 @@
     }
     if (options.sparse_linear_algebra_library_type == ACCELERATE_SPARSE) {
       *error =
-          "ACCELERATE_SPARSE is not currently supported with dynamic sparsity.";
+          "ACCELERATE_SPARSE is not currently supported with dynamic "
+          "sparsity.";
       return false;
     }
   }
@@ -310,7 +303,8 @@
       options.residual_blocks_for_subset_preconditioner.empty()) {
     *error =
         "When using SUBSET preconditioner, "
-        "Solver::Options::residual_blocks_for_subset_preconditioner cannot be "
+        "Solver::Options::residual_blocks_for_subset_preconditioner cannot "
+        "be "
         "empty";
     return false;
   }
@@ -348,8 +342,8 @@
 
   // Warn user if they have requested BISECTION interpolation, but constraints
   // on max/min step size change during line search prevent bisection scaling
-  // from occurring. Warn only, as this is likely a user mistake, but one which
-  // does not prevent us from continuing.
+  // from occurring. Warn only, as this is likely a user mistake, but one
+  // which does not prevent us from continuing.
   if (options.line_search_interpolation_type == ceres::BISECTION &&
       (options.max_line_search_step_contraction > 0.5 ||
        options.min_line_search_step_contraction < 0.5)) {
diff --git a/internal/ceres/sparse_cholesky.cc b/internal/ceres/sparse_cholesky.cc
index fe7412c..22df3c9 100644
--- a/internal/ceres/sparse_cholesky.cc
+++ b/internal/ceres/sparse_cholesky.cc
@@ -94,10 +94,10 @@
   }
 
   if (options.max_num_refinement_iterations > 0) {
-    std::unique_ptr<IterativeRefiner> refiner(
-        new IterativeRefiner(options.max_num_refinement_iterations));
-    sparse_cholesky = std::unique_ptr<SparseCholesky>(new RefinedSparseCholesky(
-        std::move(sparse_cholesky), std::move(refiner)));
+    auto refiner = std::make_unique<SparseIterativeRefiner>(
+        options.max_num_refinement_iterations);
+    sparse_cholesky = std::make_unique<RefinedSparseCholesky>(
+        std::move(sparse_cholesky), std::move(refiner));
   }
   return sparse_cholesky;
 }
@@ -118,7 +118,7 @@
 
 RefinedSparseCholesky::RefinedSparseCholesky(
     std::unique_ptr<SparseCholesky> sparse_cholesky,
-    std::unique_ptr<IterativeRefiner> iterative_refiner)
+    std::unique_ptr<SparseIterativeRefiner> iterative_refiner)
     : sparse_cholesky_(std::move(sparse_cholesky)),
       iterative_refiner_(std::move(iterative_refiner)) {}
 
diff --git a/internal/ceres/sparse_cholesky.h b/internal/ceres/sparse_cholesky.h
index feea7aa..9907d07 100644
--- a/internal/ceres/sparse_cholesky.h
+++ b/internal/ceres/sparse_cholesky.h
@@ -112,14 +112,15 @@
                                              std::string* message);
 };
 
-class IterativeRefiner;
+class SparseIterativeRefiner;
 
 // Computes an initial solution using the given instance of
-// SparseCholesky, and then refines it using the IterativeRefiner.
+// SparseCholesky, and then refines it using the SparseIterativeRefiner.
 class CERES_NO_EXPORT RefinedSparseCholesky final : public SparseCholesky {
  public:
-  RefinedSparseCholesky(std::unique_ptr<SparseCholesky> sparse_cholesky,
-                        std::unique_ptr<IterativeRefiner> iterative_refiner);
+  RefinedSparseCholesky(
+      std::unique_ptr<SparseCholesky> sparse_cholesky,
+      std::unique_ptr<SparseIterativeRefiner> iterative_refiner);
   ~RefinedSparseCholesky() override;
 
   CompressedRowSparseMatrix::StorageType StorageType() const override;
@@ -131,7 +132,7 @@
 
  private:
   std::unique_ptr<SparseCholesky> sparse_cholesky_;
-  std::unique_ptr<IterativeRefiner> iterative_refiner_;
+  std::unique_ptr<SparseIterativeRefiner> iterative_refiner_;
   CompressedRowSparseMatrix* lhs_ = nullptr;
 };
 
diff --git a/internal/ceres/sparse_cholesky_test.cc b/internal/ceres/sparse_cholesky_test.cc
index 31dbb02..0522232 100644
--- a/internal/ceres/sparse_cholesky_test.cc
+++ b/internal/ceres/sparse_cholesky_test.cc
@@ -209,7 +209,8 @@
                        ::testing::Values(OrderingType::NESDIS),
                        ::testing::Values(true, false)),
     ParamInfoToString);
-#endif  // !defined(CERES_NO_SUITESPARSE) && !defined(CERES_NO_CHOLMOD_PARTITION)
+#endif  // !defined(CERES_NO_SUITESPARSE) &&
+        // !defined(CERES_NO_CHOLMOD_PARTITION)
 
 #ifndef CERES_NO_ACCELERATE_SPARSE
 INSTANTIATE_TEST_SUITE_P(
@@ -283,9 +284,9 @@
                                            std::string* message));
 };
 
-class MockIterativeRefiner : public IterativeRefiner {
+class MockSparseIterativeRefiner : public SparseIterativeRefiner {
  public:
-  MockIterativeRefiner() : IterativeRefiner(1) {}
+  MockSparseIterativeRefiner() : SparseIterativeRefiner(1) {}
   MOCK_METHOD4(Refine,
                void(const SparseMatrix& lhs,
                     const double* rhs,
@@ -298,14 +299,15 @@
 
 TEST(RefinedSparseCholesky, StorageType) {
   auto* mock_sparse_cholesky = new MockSparseCholesky;
-  auto* mock_iterative_refiner = new MockIterativeRefiner;
+  auto* mock_iterative_refiner = new MockSparseIterativeRefiner;
   EXPECT_CALL(*mock_sparse_cholesky, StorageType())
       .Times(1)
       .WillRepeatedly(
           Return(CompressedRowSparseMatrix::StorageType::UPPER_TRIANGULAR));
   EXPECT_CALL(*mock_iterative_refiner, Refine(_, _, _, _)).Times(0);
   std::unique_ptr<SparseCholesky> sparse_cholesky(mock_sparse_cholesky);
-  std::unique_ptr<IterativeRefiner> iterative_refiner(mock_iterative_refiner);
+  std::unique_ptr<SparseIterativeRefiner> iterative_refiner(
+      mock_iterative_refiner);
   RefinedSparseCholesky refined_sparse_cholesky(std::move(sparse_cholesky),
                                                 std::move(iterative_refiner));
   EXPECT_EQ(refined_sparse_cholesky.StorageType(),
@@ -314,13 +316,14 @@
 
 TEST(RefinedSparseCholesky, Factorize) {
   auto* mock_sparse_cholesky = new MockSparseCholesky;
-  auto* mock_iterative_refiner = new MockIterativeRefiner;
+  auto* mock_iterative_refiner = new MockSparseIterativeRefiner;
   EXPECT_CALL(*mock_sparse_cholesky, Factorize(_, _))
       .Times(1)
       .WillRepeatedly(Return(LinearSolverTerminationType::SUCCESS));
   EXPECT_CALL(*mock_iterative_refiner, Refine(_, _, _, _)).Times(0);
   std::unique_ptr<SparseCholesky> sparse_cholesky(mock_sparse_cholesky);
-  std::unique_ptr<IterativeRefiner> iterative_refiner(mock_iterative_refiner);
+  std::unique_ptr<SparseIterativeRefiner> iterative_refiner(
+      mock_iterative_refiner);
   RefinedSparseCholesky refined_sparse_cholesky(std::move(sparse_cholesky),
                                                 std::move(iterative_refiner));
   CompressedRowSparseMatrix m(1, 1, 1);
@@ -331,14 +334,15 @@
 
 TEST(RefinedSparseCholesky, FactorAndSolveWithUnsuccessfulFactorization) {
   auto* mock_sparse_cholesky = new MockSparseCholesky;
-  auto* mock_iterative_refiner = new MockIterativeRefiner;
+  auto* mock_iterative_refiner = new MockSparseIterativeRefiner;
   EXPECT_CALL(*mock_sparse_cholesky, Factorize(_, _))
       .Times(1)
       .WillRepeatedly(Return(LinearSolverTerminationType::FAILURE));
   EXPECT_CALL(*mock_sparse_cholesky, Solve(_, _, _)).Times(0);
   EXPECT_CALL(*mock_iterative_refiner, Refine(_, _, _, _)).Times(0);
   std::unique_ptr<SparseCholesky> sparse_cholesky(mock_sparse_cholesky);
-  std::unique_ptr<IterativeRefiner> iterative_refiner(mock_iterative_refiner);
+  std::unique_ptr<SparseIterativeRefiner> iterative_refiner(
+      mock_iterative_refiner);
   RefinedSparseCholesky refined_sparse_cholesky(std::move(sparse_cholesky),
                                                 std::move(iterative_refiner));
   CompressedRowSparseMatrix m(1, 1, 1);
@@ -352,8 +356,8 @@
 
 TEST(RefinedSparseCholesky, FactorAndSolveWithSuccess) {
   auto* mock_sparse_cholesky = new MockSparseCholesky;
-  std::unique_ptr<MockIterativeRefiner> mock_iterative_refiner(
-      new MockIterativeRefiner);
+  std::unique_ptr<MockSparseIterativeRefiner> mock_iterative_refiner(
+      new MockSparseIterativeRefiner);
   EXPECT_CALL(*mock_sparse_cholesky, Factorize(_, _))
       .Times(1)
       .WillRepeatedly(Return(LinearSolverTerminationType::SUCCESS));
@@ -363,7 +367,7 @@
   EXPECT_CALL(*mock_iterative_refiner, Refine(_, _, _, _)).Times(1);
 
   std::unique_ptr<SparseCholesky> sparse_cholesky(mock_sparse_cholesky);
-  std::unique_ptr<IterativeRefiner> iterative_refiner(
+  std::unique_ptr<SparseIterativeRefiner> iterative_refiner(
       std::move(mock_iterative_refiner));
   RefinedSparseCholesky refined_sparse_cholesky(std::move(sparse_cholesky),
                                                 std::move(iterative_refiner));